How AEO Works: The Documentation
A working reference for the technical and operational mechanics behind answer engine optimization, written for teams implementing it themselves, not just reading about it.
The Four AEO Pillars
AEO work breaks into four areas: a technical foundation (schema markup, llms.txt, robots.txt tuned for AI crawlers), content shaped for extraction, off-site citation building, and an owned knowledge graph. The technical foundation comes first; the other three pillars have nowhere to point if a site is not readable to GPTBot, ClaudeBot, and PerplexityBot to begin with.
Technical Foundation
Schema markup (JSON-LD) explicitly labels entities and facts so crawlers don’t have to infer them. llms.txt is a plain-text file at a site’s root, similar to robots.txt, listing canonical pages grouped by category for AI assistants to reference. robots.txt itself should be checked to confirm it isn’t blocking gptbot, claudebot, perplexitybot, or google-extended; most sites already allow them by default, but it’s worth verifying rather than assuming.
Content Shaped for Extraction
LLMs disproportionately extract specific content shapes: the first 200 words of a page, FAQ blocks structured as discrete question-answer pairs, comparison tables cited verbatim for “X vs Y” queries, and definitional content for category vocabulary. A page can have excellent information and still be poorly shaped for extraction if it buries the direct answer inside narrative prose instead of leading with it.
Off-Site Citation Building
LLMs weight third-party corroboration heavily: Reddit threads, G2 and Capterra reviews, and YouTube transcripts all feed into what gets cited. This is sustained participation, not one-off posting: a well-established subreddit thread can outrank a much larger competitor’s site for the same query because real-user language reads as more authoritative than marketing copy.
Measuring Success
Track three things on a regular cadence: prompt-level visibility (a fixed set of buyer-relevant prompts checked periodically in ChatGPT and Perplexity for citations), branded search volume (a leading indicator that AEO is compounding), and inbound attribution (a simple “how did you hear about us” field surfaces AI-driven leads within a few months).